<p><b>Abstract</b>—Support Vector Tracking (<b>SVT</b>) integrates the Support Vector Machine (<b>SVM</b>) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, <b>SVT</b> maximizes the <b>SVM</b> classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using <b>SVT</b> for vehicle tracking in image sequences.</p>